Duplicate from tner/bc5cdr

Co-authored-by: Asahi Ushio <asahi417@users.noreply.huggingface.co>

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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README.md

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+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+pretty_name: BioCreative V CDR
+---
+
+# Dataset Card for "tner/bc5cdr"
+
+## Dataset Description
+
+- **Repository:** [T-NER](https://github.com/asahi417/tner)
+- **Paper:** [https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true)
+- **Dataset:** BioCreative V CDR
+- **Domain:** Biomedical
+- **Number of Entity:** 2
+
+### Dataset Summary
+BioCreative V CDR NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
+The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.
+- Entity Types: `Chemical`, `Disease`
+
+## Dataset Structure
+
+### Data Instances
+An example of `train` looks as follows.
+
+```
+{
+    'tags': [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],
+    'tokens': ['Fasciculations', 'in', 'six', 'areas', 'of', 'the', 'body', 'were', 'scored', 'from', '0', 'to', '3', 'and', 'summated', 'as', 'a', 'total', 'fasciculation', 'score', '.']
+}
+```
+
+### Label ID
+The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/bc5cdr/raw/main/dataset/label.json).
+```python
+{
+    "O": 0,
+    "B-Chemical": 1,
+    "B-Disease": 2,
+    "I-Disease": 3,
+    "I-Chemical": 4
+}
+```
+
+### Data Splits
+
+|  name   |train|validation|test|
+|---------|----:|---------:|---:|
+|bc5cdr|5228|      5330|5865|
+
+### Citation Information
+
+```
+@article{wei2016assessing,
+  title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task},
+  author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and Lu, Zhiyong},
+  journal={Database},
+  volume={2016},
+  year={2016},
+  publisher={Oxford Academic}
+}
+```

bc5cdr.py

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+""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """
+import json
+from itertools import chain
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """[Bio Creative 5 CDR NER dataset](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true)"""
+_NAME = "bc5cdr"
+_VERSION = "1.0.0"
+_CITATION = """
+@article{wei2016assessing,
+  title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task},
+  author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and Lu, Zhiyong},
+  journal={Database},
+  volume={2016},
+  year={2016},
+  publisher={Oxford Academic}
+}
+"""
+
+_HOME_PAGE = "https://github.com/asahi417/tner"
+_URL = f'https://huggingface.co/datasets/tner/{_NAME}/raw/main/dataset'
+_URLS = {
+    str(datasets.Split.TEST): [f'{_URL}/test.json'],
+    str(datasets.Split.TRAIN): [f'{_URL}/train.json'],
+    str(datasets.Split.VALIDATION): [f'{_URL}/valid.json'],
+}
+
+
+class BC5CDRConfig(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(BC5CDRConfig, self).__init__(**kwargs)
+
+
+class BC5CDR(datasets.GeneratorBasedBuilder):
+    """Dataset."""
+
+    BUILDER_CONFIGS = [
+        BC5CDRConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
+    ]
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URLS)
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
+
+    def _generate_examples(self, filepaths):
+        _key = 0
+        for filepath in filepaths:
+            logger.info(f"generating examples from = {filepath}")
+            with open(filepath, encoding="utf-8") as f:
+                _list = [i for i in f.read().split('\n') if len(i) > 0]
+                for i in _list:
+                    data = json.loads(i)
+                    yield _key, data
+                    _key += 1
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "tags": datasets.Sequence(datasets.Value("int32")),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOME_PAGE,
+            citation=_CITATION,
+        )

dataset/label.json

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+{"O": 0, "B-Chemical": 1, "B-Disease": 2, "I-Disease": 3, "I-Chemical": 4}